Sentiment Analysis of Spotify Data - EDA Part
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(spotifyr) # spotify api
library(tidytext)##
## Attaching package: 'tidytext'
##
## The following object is masked from 'package:spotifyr':
##
## tidylibrary(rgenius) # genius api
library(wordcloud)## Loading required package: RColorBrewerlibrary(wordcloud2)# Load ggridges package
library(ggridges)devtools::install_github('charlie86/spotifyr') # spotify ## Skipping install of 'spotifyr' from a github remote, the SHA1 (45ffa4ee) has not changed since last install.
## Use `force = TRUE` to force installation# Authenticate Spotify
access_token <- get_spotify_access_token(
client_id="694b3d6cc44c4bc9b1ddd0debb625714",
client_secret="00012038b8bb439eb1d31a673f02a78f")# Set up authentication for Genius API
Sys.setenv(GENIUS_API_TOKEN = 'svE9xETWjRF_YczqaEIna2dBChd6_6EegcxhabQkk32O-8OzFoWRkwsL4YtCd11p')Extracting Audio Features of Harry Styles songs
Harry_styles <- get_artist_audio_features('Harry Styles')Extracting all albums of Harry Styles
Harry <- get_artist_albums('6KImCVD70vtIoJWnq6nGn3')# creating an empty list to store the data frames
album_list <- list()
# iterating over the id values in harry data frame
for (id in Harry$id) {
# calling get_albums() function for each id and store the result in a data frame
album_data <- get_albums(id)
# adding the resulting data frame to the list
album_list[[id]] <- album_data
}
# combining all data frames into a single data frame using the bind_rows() function
albums_df <- bind_rows(album_list)Columns of Audio Features Extracted df
colnames(Harry_styles)## [1] "artist_name" "artist_id"
## [3] "album_id" "album_type"
## [5] "album_images" "album_release_date"
## [7] "album_release_year" "album_release_date_precision"
## [9] "danceability" "energy"
## [11] "key" "loudness"
## [13] "mode" "speechiness"
## [15] "acousticness" "instrumentalness"
## [17] "liveness" "valence"
## [19] "tempo" "track_id"
## [21] "analysis_url" "time_signature"
## [23] "artists" "available_markets"
## [25] "disc_number" "duration_ms"
## [27] "explicit" "track_href"
## [29] "is_local" "track_name"
## [31] "track_preview_url" "track_number"
## [33] "type" "track_uri"
## [35] "external_urls.spotify" "album_name"
## [37] "key_name" "mode_name"
## [39] "key_mode"Renaming column name for easy understanding
Harry_styles <- Harry_styles %>% rename("id" = "album_id")
colnames(Harry_styles)## [1] "artist_name" "artist_id"
## [3] "id" "album_type"
## [5] "album_images" "album_release_date"
## [7] "album_release_year" "album_release_date_precision"
## [9] "danceability" "energy"
## [11] "key" "loudness"
## [13] "mode" "speechiness"
## [15] "acousticness" "instrumentalness"
## [17] "liveness" "valence"
## [19] "tempo" "track_id"
## [21] "analysis_url" "time_signature"
## [23] "artists" "available_markets"
## [25] "disc_number" "duration_ms"
## [27] "explicit" "track_href"
## [29] "is_local" "track_name"
## [31] "track_preview_url" "track_number"
## [33] "type" "track_uri"
## [35] "external_urls.spotify" "album_name"
## [37] "key_name" "mode_name"
## [39] "key_mode"Column names of Albums extracted df
colnames(Harry)## [1] "album_group" "album_type" "artists"
## [4] "available_markets" "href" "id"
## [7] "images" "name" "release_date"
## [10] "release_date_precision" "total_tracks" "type"
## [13] "uri" "external_urls.spotify"Combined df
colnames(albums_df)## [1] "album_group" "album_type" "artists"
## [4] "available_markets" "copyrights" "genres"
## [7] "href" "id" "images"
## [10] "label" "name" "popularity"
## [13] "release_date" "release_date_precision" "total_tracks"
## [16] "type" "uri" "external_ids.upc"
## [19] "external_urls.spotify" "tracks.href" "tracks.items"
## [22] "tracks.limit" "tracks.next" "tracks.offset"
## [25] "tracks.previous" "tracks.total"# Merging all 3 dataframes
merged_df <- full_join(Harry, albums_df, by = "id")
# joining the resulting data frame with the tracks_df data frame by the id column
merged_df <- full_join(merged_df, Harry_styles, by = "id")
# checking the resulting data frame
merged_dfRemoving redundant and unnecarry columns from the dataframe
df <- select(merged_df, -name.x, -release_date.x, -total_tracks.x, -uri.x, -type.x, -artists.y, -album_group.x, -album_type.x, -artists.x, -images.x, -external_ids.upc, -available_markets.x, -href.x, -external_urls.spotify.x, -release_date_precision.x, -album_group.y, -album_type.y, -available_markets.y, -copyrights, -href.y, -images.y, -name.y, -release_date.y, -release_date_precision.y, -total_tracks.y, -type.y, -uri.y, -external_urls.spotify.y, -tracks.href,-tracks.limit, -tracks.next, -tracks.offset, -tracks.previous, -analysis_url, -available_markets, -explicit, -disc_number, -track_href, -is_local, -track_preview_url, -type, -track_uri, -external_urls.spotify, -external_urls.spotify)head(df,5)colnames(df)## [1] "id" "genres"
## [3] "label" "popularity"
## [5] "tracks.items" "tracks.total"
## [7] "artist_name" "artist_id"
## [9] "album_type" "album_images"
## [11] "album_release_date" "album_release_year"
## [13] "album_release_date_precision" "danceability"
## [15] "energy" "key"
## [17] "loudness" "mode"
## [19] "speechiness" "acousticness"
## [21] "instrumentalness" "liveness"
## [23] "valence" "tempo"
## [25] "track_id" "time_signature"
## [27] "artists" "duration_ms"
## [29] "track_name" "track_number"
## [31] "album_name" "key_name"
## [33] "mode_name" "key_mode"# group by the id column
ged_df <- df %>%
group_by(id) %>%
# sort each group by the popularity column in descending order
arrange(desc(popularity))
# checking the resulting data frame
head(ged_df, 5)ggplot(df, aes(x = album_name, y = tracks.total, fill = artist_name)) +
geom_bar(stat = "identity", color = "black", alpha = 0.8) +
scale_fill_brewer(palette = "Paired") +
labs(x = "Album Name", y = "Total Tracks", title = "Total Tracks by Album",
subtitle = "Grouped by Artist Name", fill = "Artist Name") +
theme_minimal() +
theme(plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 14),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10))
## As we see a lot NA values for Harry Styles we have limited album data
so, decided to get rid of NA rows.
# drop rows where any of the column value is NA
df <- na.omit(df)
head(df,5)Now plotting after removing NA rows.
ggplot(df, aes(x = album_name, y = tracks.total, fill = artist_name)) +
geom_bar(stat = "identity", color = "black", alpha = 0.8) +
scale_fill_brewer(palette = "Paired") +
labs(x = "Album Name", y = "Total Tracks", title = "Total Tracks by Album",
subtitle = "Grouped by Artist Name", fill = "Artist Name") +
theme_minimal() +
theme(plot.title = element_text(size = 18, face = "bold"),
plot.subtitle = element_text(size = 14),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10))
Extracting unique album ids
unique(df$id)## [1] "5r36AJ6VOJtp00oxSkBZ5h" "7xV2TzoaVc0ycW7fwBwAml" "1FZKIm3JVDCxTchXDo5jOV"Plotting Density plots for different features.
ggplot(df, aes(x = danceability, fill = album_name)) +
geom_density(alpha = 0.4) +
scale_fill_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
labs(title = "Distribution of Danceability by Album",
x = "Value", y = "Density") +
theme_minimal()
ggplot(df, aes(x = valence, fill = album_name)) +
geom_density(alpha = 0.4) +
scale_fill_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
labs(title = "Distribution of Valence by Album",
x = "Value", y = "Density") +
theme_minimal()
ggplot(df, aes(x = loudness, fill = album_name)) +
geom_density(alpha = 0.4) +
scale_fill_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
labs(title = "Distribution of Loudness by Album",
x = "Value", y = "Density") +
theme_minimal()
ggplot(df, aes(x = tempo, fill = album_name)) +
geom_density(alpha = 0.4) +
scale_fill_manual(values = c("#E69F00", "#56B4E9", "#009E73")) +
labs(title = "Distribution of Tempo by Album",
x = "Value", y = "Density") +
theme_minimal()
ggplot(df, aes(x = album_name, fill = key_mode)) +
geom_bar(position = "stack", alpha = 0.8) +
labs(title = "Key Note Distribution by Album", x = "Album", y = "Count", fill = "Key Note") +
theme_minimal()
gaga <- get_artist_audio_features('Lady Gaga')gagaExtracting Genre wise comparison data
# Define the genres for which you want to get the top 5 artists
genres <- c("rock", "pop", "hip-hop", "electronic", "country")
# Initialize an empty data frame to store the results
df <- data.frame()
# Loop over the genres and get the top 5 artists for each genre
for (genre in genres) {
artists <- get_genre_artists(genre = genre, limit = 5)
df <- rbind(df, data.frame(genre = genre, artist = artists$name))
}# Filter df to create a data frame of the top 5 artists for each genre
rock_data <- df[df$genre == "rock", ]
pop_data <- df[df$genre == "pop", ]
hiphop_data <- df[df$genre == "hip-hop", ]
electronic_data <- df[df$genre == "electronic", ]
country_data <- df[df$genre == "country", ]rock_data# Create a bar chart of the top artists for each genre
ggplot(df, aes(x = artist, fill = genre)) +
geom_bar() +
coord_flip() +
labs(x = "Artist", y = "Number of Plays", title = "Top 5 Artists by Genre")
Generating word Clouds for each artist in each genre
library(wordcloud)
# Loop over the genres and get the top 5 artists for each genre
for (genre in genres) {
artists <- get_genre_artists(genre = genre, limit = 10)
# Loop over the artists and get the top tracks
for (artist_id in artists$id) {
top_tracks <- get_artist_top_tracks(artist_id)
track_names <- top_tracks$name
# Generate a word cloud for the artist's top tracks
wordcloud(track_names, scale=c(3,0.5), min.freq=1, max.words=15, random.order=FALSE, colors=brewer.pal(8, "Dark2"))
title(paste0("Word cloud for ", genre, " artist ", get_artist(artist_id)$name))
}
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Retrieves audio features of Billie Eilish’s albums, removes duplicates, and returns the closest matching artist’s audio features using the get_artist_audio_features function.
billie_eilish <- get_artist_audio_features(
artist = "Billie Eilish",
include_groups = "album",
return_closest_artist = TRUE,
dedupe_albums = TRUE,
market = NULL,
authorization = access_token
)Fetches albums, singles, appearances, and compilations of an artist with the given ID (Billie Eilish), up to a limit of 20, using the get_artist_albums function.
bi_id <- "6qqNVTkY8uBg9cP3Jd7DAH"
bi<- get_artist_albums(
id = bi_id,
include_groups = c("album", "single", "appears_on", "compilation"),
market = NULL,
limit = 20,
offset = 0,
authorization = access_token,
include_meta_info = FALSE
)Creates an empty list to store data frames, iterates over the
artist’s album IDs, retrieves album information using the
get_albums function, and combines all data frames into a
single data frame using the bind_rows function.
# create an empty list to store the data frames
album_list1 <- list()
# iterate over the id values in harry data frame
for (id in bi$id) {
# call get_albums() function for each id and store the result in a data frame
album_data<- get_albums(id, authorization = access_token)
# add the resulting data frame to the list
album_list1[[id]] <- album_data
}
# combine all data frames into a single data frame using the bind_rows() function
bi_albums_df <- bind_rows(album_list1)Renames the ‘album_id’ column in the Billie Eilish data frame, merges it with the album data frame on the ‘id’ column, and displays the first 5 rows of the resulting data frame.
# remaining the column
billie_eilish <- billie_eilish%>% rename("id" = "album_id")
# Merging all dataframes
bi_merged_df <- full_join(billie_eilish, bi_albums_df,by ="id")
# check the resulting data frame
head(bi_merged_df,5)unique(bi_merged_df$id)## [1] "0JGOiO34nwfUdDrD612dOp" "5tzRuO6GP7WRvP3rEOPAO9" "0S0KGZnfBGSIssfF54WSJh"
## [4] "1YPWxMpQEC8kcOuefgXbhj" "2kzPJWrTjVKEYWWhowXLnz" "5lDUpb6zBr4xBh9AGUXYtQ"
## [7] "2AyexmwyUy1nZfBadyewL7" "3ZuV4xSFJnWDncgMICfFmX" "4E8puNI8tw7cXz6YJkwMew"
## [10] "5G58VVE9ub1KE01Mvbd8XM" "3oxhQpF3Twbkl18oQYfnh5" "5sXSHscDjBez8VF20cSyad"
## [13] "4i3rAwPw7Ln2YrKDusaWyT" "6lMlX68jJrx67hiCqdiDvW" "0LgnntyagLdfW5Dz2OSYHU"
## [16] "0ifM8RTX9HjtCJtY9452bW" "2sBB17RXTamvj7Ncps15AK" "1Z0XtKcevvITZ5ydimkYcx"
## [19] "5m9lO9SriYMPpXTrVIU8P5" "5XRJoC2QtsNbAubsCrBBbG"dropping unnecessary columns
bi_df <- select(bi_merged_df, -album_type.x, -album_release_date_precision, -analysis_url, -available_markets.x, -explicit, -track_href, -is_local, -external_urls.spotify.y, -tracks.href, -tracks.limit, -tracks.next, -tracks.offset, -tracks.previous, -artists.x, -disc_number, -type.x, -track_uri, -external_urls.spotify.x, -album_type.y, -available_markets.y, -copyrights, -href, -external_ids.upc, -uri, -name, -label, -images, -type.y, -tracks.total, -track_preview_url, -release_date_precision)Groups the cleaned data frame by the ‘id’ column, sorts each group by the ‘popularity’ column in descending order, and displays the resulting data frame.
# group by the id column
bi_ged_df <- bi_df %>%
group_by(id) %>%
# sort each group by the popularity column in descending order
arrange(desc(popularity))
# check the resulting data frame
bi_ged_dfExtracts and displays the name of the first unique album from the sorted data frame.
b_alb <- head(unique(bi_ged_df$album_name),1)
b_alb## [1] "WHEN WE ALL FALL ASLEEP, WHERE DO WE GO?"considering top 10 albums
b_alb_df <- subset(bi_ged_df, album_name %in% b_alb)
b_alb_dfNext, use the same code to extract data for Taylor Swift.
taylor_swift <- get_artist_audio_features(
artist = "Taylor Swift",
include_groups = "album",
return_closest_artist = TRUE,
dedupe_albums = TRUE,
market = NULL,
authorization = access_token
)ts_id <- "06HL4z0CvFAxyc27GXpf02"
ts<- get_artist_albums(
id = ts_id,
include_groups = c("album", "single", "appears_on", "compilation"),
market = NULL,
limit = 20,
offset = 0,
authorization = access_token,
include_meta_info = FALSE
)# create an empty list to store the data frames
album_list3 <- list()
# iterate over the id values in harry data frame
for (id in ts$id) {
# call get_albums() function for each id and store the result in a data frame
album_data<- get_albums(id, authorization = access_token)
# add the resulting data frame to the list
album_list3[[id]] <- album_data
}
# combine all data frames into a single data frame using the bind_rows() function
ts_albums_df <- bind_rows(album_list3)# remaining the column
taylor_swift <- taylor_swift %>% rename("id" = "album_id")# Merging all dataframes
ts_merged_df <- full_join(taylor_swift, ts_albums_df,by ="id")
# check the resulting data frame
head(ts_merged_df,5)unique(ts_merged_df$id)## [1] "3lS1y25WAhcqJDATJK70Mq" "4894htPwC6zoiuTqUQwn4I" "151w1FgRZfnKZA9FEcg9Z3"
## [4] "4moVP48t9bji7djUc5VOvi" "6kZ42qRrzov54LcAk4onW9" "6x9s2ObPdpATZgrwxsk9c0"
## [7] "4hDok0OAJd57SGIT8xuWJH" "6AORtDjduMM3bupSWzbTSG" "1DT6fDJL6AWPJxe7Lq1dPb"
## [10] "2Xoteh7uEpea4TohMxjtaq" "40zKHK0aGM4PITqPy5vfQh" "5jmVg7rwRcgd6ARPAeYNSm"
## [13] "0PZ7lAru5FDFHuirTkWe9Z" "3VaaZ7OIbGLi60NVsnueoo" "1pzvBxYgT6OVwJLtHkrdQK"
## [16] "7v7pe5vZQPWB5zW0JrKRiw" "2fenSS68JI1h4Fo296JfGr" "0xS0iOtxQRoJvfcFcJA5Gv"
## [19] "1NAmidJlEaVgA3MpcPFYGq" "1MHuZZrGT36cXLxAQ5cLP3" "6DEjYFkNZh67HP7R9PSZvv"
## [22] "1Hrs3jLGexOvBoaPMoOQYJ" "1MPAXuTVL2Ej5x0JHiSPq8" "0bEySlRAkuPxV9KVWhXXBr"
## [25] "2QJmrSgbdM35R67eoGQo4j" "4uUAUqIfSomFTbbjGp3TYp" "6EsTJnpahwW6xX20zvqQgZ"
## [28] "0L3oaZUj3loxosjvugCLGG" "7N3xz2HFNVH5BEkm8ZVmoR" "02H4kc9YLgorpUIREOwa0q"
## [31] "34OkZVpuzBa9y40DCy0LPR" "5LVuISYu2g4YbyxYhFPIbK" "4R3hKmiJWEjRe6l03DoV9t"
## [34] "5fy0X0JmZRZnVa2UEicIOl" "1yGbNOtRIgdIiGHOEBaZWf" "1EoDsNmgTLtmwe1BDAVxV5"
## [37] "1KlU96Hw9nlvqpBPlSqcTV" "1KVKqWeRuXsJDLTW0VuD29" "4jTYApZPMapg56gRycOn0D"
## [40] "63lVCnv8B30qedCiTlAc9J" "7daMnnffzVSbNJj8Dy75Ev" "11gfxXxJPd3j6sdWUyEA5S"
## [43] "4ErTrymYK8VIBQR8J8Hjy1" "6fyR4wBPwLHKcRtxgd4sGh" "5MfAxS5zz8MlfROjGQVXhy"
## [46] "6Ar2o9KCqcyYF9J0aQP3au" "75N0Z60SNMQbAPYZuxKgWd" "3QXlUpSDgakWZK2WqQv0pF"
## [49] "1BdjHo5IR6twMhJDxzlpLt" "5EpMjweRD573ASl7uNiHym" "6GPyXXND6hIZpd9bRhCsFv"
## [52] "6S6JQWzUrJVcJLK4fi74Fw" "3Mvk2LKxfhc2KVSnDYC40I" "6tgMb6LEwb3yj7BdYy462y"
## [55] "08CWGiv27MVQhYpuTtvx83" "2dqn5yOQWdyGwOpOIi9O4x" "3EzFY9Rg0PpbADMth746zi"
## [58] "1CYlmaXajTC59VJWSSeE7Y" "6vRfYCQ1mKKfnB6D7R4N5p" "2gP2LMVcIFgVczSJqn340t"
## [61] "43OpbkiiIxJO8ktIB777Nn" "1ycoesYxIFymXWebfmz828" "1rwH2628RIOVM3WMwwO418"
## [64] "5eyZZoQEFQWRHkV2xgAeBw" "7mzrIsaAjnXihW3InKjlC3" "2rU7u7C2v5i45MFVxx7xG1"
## [67] "1mFGeuBwVfAyli6aDoy9OI" "1ymIvQpnPQBj1lGlJRqrFQ"# dropping unnecessary columns
ts_df <- select(ts_merged_df, -album_type.x, -album_release_date_precision, -analysis_url, -available_markets.x, -explicit, -track_href, -is_local, -external_urls.spotify.y, -tracks.href, -tracks.limit, -tracks.next, -tracks.offset, -tracks.previous, -artists.x, -disc_number, -type.x, -track_uri, -external_urls.spotify.x, -album_type.y, -available_markets.y, -copyrights, -href, -external_ids.upc, -uri, -name, -label, -images, -type.y, -tracks.total, -track_preview_url, -release_date_precision)# group by the id column
ts_ged_df <- ts_df %>%
group_by(id) %>%
# sort each group by the popularity column in descending order
arrange(desc(popularity))
# check the resulting data frame
ts_ged_dfts_alb <- head(unique(ts_ged_df$album_name),1)
ts_alb## [1] "Midnights"Next, use the same code to extract data for Linkin Park.
Linkin_Park <- get_artist_audio_features(
artist = "Linkin Park",
include_groups = "album",
return_closest_artist = TRUE,
dedupe_albums = TRUE,
market = NULL,
authorization = access_token
)LP_id <- "6XyY86QOPPrYVGvF9ch6wz"
LP_albums <- get_artist_albums(
id = LP_id,
include_groups = c("album", "single", "appears_on", "compilation"),
market = NULL,
limit = 20,
offset = 0,
authorization = access_token,
include_meta_info = FALSE
)# Create an empty list to store the data frames
album_list2 <- list()
# Iterate over the id values in the artist_albums data frame
for (id in LP_albums$id) {
album_data1 <- get_albums(id, authorization = access_token)
# Add the resulting data frame to the list
album_list2[[id]] <- album_data1
}
# Combine all data frames into a single data frame using the bind_rows() function
LP_albums_df <- bind_rows(album_list2)Linkin_Park <- Linkin_Park %>% rename("id" = "album_id")# Merging all dataframes
LP_merged_df <- full_join(LP_albums_df, LP_albums, by = "id")
LP_merged_df <- full_join(LP_merged_df, Linkin_Park, by = "id")LP_df <- select(LP_merged_df, -name.x, -release_date.x, -total_tracks.x, -uri.x, -type.x, -artists.y, -album_group.x, -album_type.x, -artists.x, -images.x, -external_ids.upc, -available_markets.x, -href.x, -external_urls.spotify.x, -release_date_precision.x, -album_group.y, -album_type.y, -available_markets.y, -copyrights, -href.y, -images.y, -name.y, -release_date.y, -release_date_precision.y, -total_tracks.y, -type.y, -uri.y, -external_urls.spotify.y, -tracks.href,-tracks.limit, -tracks.next, -tracks.offset, -tracks.previous, -analysis_url, -available_markets, -explicit, -disc_number, -track_href, -is_local, -track_preview_url, -type, -track_uri, -external_urls.spotify, -external_urls.spotify)# Group by the id column and sort each group by the popularity column in descending order
LP_grouped_df <- LP_df %>%
group_by(id) %>%
arrange(desc(popularity))lp_alb <- head(unique(LP_grouped_df$album_name),1)
lp_alb## [1] "Meteora 20th Anniversary Edition"Lastly, use the same code to extract data for Eminem.
eminem <- get_artist_audio_features(
artist = "Eminem",
include_groups = "album",
return_closest_artist = TRUE,
dedupe_albums = TRUE,
market = NULL,
authorization = access_token
)em_id <- "7dGJo4pcD2V6oG8kP0tJRR"
em_albums <- get_artist_albums(
id = em_id,
include_groups = c("album", "single", "appears_on", "compilation"),
market = NULL,
limit = 20,
offset = 0,
authorization = access_token,
include_meta_info = FALSE
)# Create an empty list to store the data frames
album_list4 <- list()
# Iterate over the id values in the artist_albums data frame
for (id in em_albums$id) {
album_data <- get_albums(id, authorization = access_token)
# Add the resulting data frame to the list
album_list4[[id]] <- album_data
}
# Combine all data frames into a single data frame using the bind_rows() function
em_albums_df <- bind_rows(album_list4)eminem <- eminem %>% rename("id" = "album_id")# Merging all dataframes
em_merged_df <- full_join(em_albums_df, em_albums, by = "id")
em_merged_df <- full_join(em_merged_df, eminem, by = "id")em_df <- select(em_merged_df, -name.x, -release_date.x, -total_tracks.x, -uri.x, -type.x, -artists.y, -album_group.x, -album_type.x, -artists.x, -images.x, -external_ids.upc, -available_markets.x, -href.x, -external_urls.spotify.x, -release_date_precision.x, -album_group.y, -album_type.y, -available_markets.y, -copyrights, -href.y, -images.y, -name.y, -release_date.y, -release_date_precision.y, -total_tracks.y, -type.y, -uri.y, -external_urls.spotify.y, -tracks.href,-tracks.limit, -tracks.next, -tracks.offset, -tracks.previous, -analysis_url, -available_markets, -explicit, -disc_number, -track_href, -is_local, -track_preview_url, -type, -track_uri, -external_urls.spotify, -external_urls.spotify)# Group by the id column and sort each group by the popularity column in descending order
em_grouped_df <- em_df %>%
group_by(id) %>%
arrange(desc(popularity))em_alb <- head(unique(em_grouped_df$album_name),1)
em_alb## [1] "Music To Be Murdered By"# Filter the data frames for the most popular album for each artist
ts_filtered <- ts_ged_df %>% dplyr::filter(album_name == "Midnights")
em_filtered <- em_grouped_df %>% dplyr::filter(album_name == "Music To Be Murdered By")
lp_filtered <- LP_grouped_df %>% dplyr::filter(album_name == "Meteora 20th Anniversary Edition")
be_filtered <- bi_ged_df %>% dplyr::filter(album_name == "WHEN WE ALL FALL ASLEEP, WHERE DO WE GO?")
# Combine the filtered data frames into one combined data frame
combined_df <- bind_rows(ts_filtered, em_filtered, lp_filtered, be_filtered)
# Keep only the relevant columns
combined_df <- combined_df %>% dplyr::select(artist_name, album_name, id,track_name, danceability,valence, speechiness,tempo, key_mode)
combined_df# Plot a box plot for valence
boxplot_valence <-ggplot(combined_df, aes(x = artist_name, y = valence, fill = artist_name)) +
geom_boxplot(alpha=0.6) +
labs(title = "Valence Distribution by Artist",
x = "Artist Name", y = "Valence Score") +
theme_minimal() +
theme(plot.title = element_text(size = 18, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10)) +
guides(fill = FALSE)## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.boxplot_valence 
library(tidyverse)
stats <- combined_df %>%
group_by(artist_name) %>%
summarise(
min_tempo = min(tempo),
max_tempo = max(tempo),
q1_tempo = quantile(tempo, 0.25),
median_tempo = median(tempo),
q3_tempo = quantile(tempo, 0.75)
)
statsOverall, Eminem and Linkin Park have a wider range of valence scores, suggesting their music covers a broader spectrum of emotions, while Billie Eilish and Taylor Swift tend to have lower valence scores, indicating a more somber or melancholic tone in their music.
# Plot a box plot for danceability
boxplot_dance <-ggplot(combined_df, aes(x = artist_name, y = danceability, fill = artist_name)) +
geom_boxplot(alpha=0.6) +
labs(title = "Danceability Distribution by Artist",
x = "Artist Name", y = "Danceability Score") +
theme_minimal() +
theme(plot.title = element_text(size = 18, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10)) +
guides(fill = FALSE)
boxplot_dance
In summary, Eminem’s music tends to have the highest danceability, followed by Billie Eilish and Taylor Swift. Linkin Park’s music generally has the lowest danceability among the four artists.
# Plot a box plot for speechiness
boxplot_speech <- ggplot(combined_df, aes(x = artist_name, y = speechiness, fill = artist_name)) +
geom_boxplot(alpha=0.6) +
labs(title = "Speechiness Distribution by Artist",
x = "Artist Name", y = "Speechiness Score") +
theme_minimal() +
theme(plot.title = element_text(size = 18, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10)) +
guides(fill = FALSE)
boxplot_speech
# Plot a box plot for tempo
boxplot_tempo <- ggplot(combined_df, aes(x = artist_name, y = tempo, fill = artist_name)) +
geom_boxplot(alpha=0.6) +
labs(title = "Tempo Distribution by Artist",
x = "Artist Name", y = "Tempo Score") +
theme_minimal() +
theme(plot.title = element_text(size = 18, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10)) +
guides(fill = FALSE)
boxplot_tempo
Overall, the tempo of the four artists’ music varies, with Eminem generally having higher tempos, followed by Linkin Park, Taylor Swift, and Billie Eilish. The tempo ranges and median values suggest that each artist has a distinct musical style, with varying levels of energy and pacing.
custom_colors1 <- c("#FF0000", "#FF7F00", "#FFFF00", "#7FFF00", "#00FF00", "#00FF7F", "#00FFFF", "#007FFF", "#0000FF", "#7F00FF", "#FF00FF", "#FF007F", "#8B0000", "#FF4500", "#FFD700", "#ADFF2F", "#32CD32", "#66CDAA", "#00CED1", "#4682B4", "#483D8B", "#9400D3", "#FF1493")
# Create a stacked bar plot
key_plot <-ggplot(combined_df, aes(x = artist_name, fill = key_mode)) +
geom_bar(position = "stack", alpha = 0.8) +
labs(title = "Key Distribution by Artist", x = "Artist", y = "Count", fill = "Key Mode") +
scale_fill_manual(name = "Key Mode", values = c(custom_colors1, "grey")) +
theme_minimal() +
theme(plot.title = element_text(size = 18, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 10))
key_plot
library(dplyr)
# Create the data frame from the given data
key_data <- data.frame(
artist_name = c(rep("Billie Eilish", 10), rep("Eminem", 12), rep("Linkin Park", 21), rep("Taylor Swift", 9)),
key_mode = c("A major", "C major", "C# major", "D# major", "E major", "F minor", "F# minor", "G major", "G# major", "G# minor",
"A major", "A# minor", "C# major", "D major", "D minor", "D# major", "D# minor", "E minor", "F major", "F minor", "F# minor", "G# major",
"A major", "A minor", "A# major", "A# minor", "B major", "B minor", "C major", "C minor", "C# major", "C# minor", "D major", "D# major", "D# minor", "E major", "E minor", "F# major", "F# minor", "G major", "G minor", "G# major", "G# minor",
"A major", "A# major", "C major", "D major", "E major", "E minor", "G major", "G minor", "G# major"),
count = c(1, 2, 1, 1, 2, 1, 1, 2, 1, 2,
2, 6, 11, 3, 2, 1, 1, 5, 3, 2, 3, 1,
8, 1, 1, 9, 4, 1, 1, 1, 16, 8, 4, 1, 6, 4, 5, 5, 4, 2, 1, 3, 3,
2, 1, 6, 2, 4, 2, 6, 1, 2))
# Group by artist_name and arrange in descending order of count
key_data_grouped <- key_data %>%
group_by(artist_name) %>%
arrange(desc(count))
# Get the top 3 most used keys for each artist
top_keys <- key_data_grouped %>%
group_by(artist_name) %>%
slice_max(count, n = 3)
# Display the top 3 keys for each artist
print(top_keys)## # A tibble: 14 × 3
## # Groups: artist_name [4]
## artist_name key_mode count
## <chr> <chr> <dbl>
## 1 Billie Eilish C major 2
## 2 Billie Eilish E major 2
## 3 Billie Eilish G major 2
## 4 Billie Eilish G# minor 2
## 5 Eminem C# major 11
## 6 Eminem A# minor 6
## 7 Eminem E minor 5
## 8 Linkin Park C# major 16
## 9 Linkin Park A# minor 9
## 10 Linkin Park A major 8
## 11 Linkin Park C# minor 8
## 12 Taylor Swift C major 6
## 13 Taylor Swift G major 6
## 14 Taylor Swift E major 4